Search Results for author: Tetsunori Kobayashi

Found 13 papers, 1 papers with code

An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR

no code implementations20 Oct 2021 Huaibo Zhao, Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi

In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency.

Automatic Speech Recognition

Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units

1 code implementation8 Oct 2021 Yosuke Higuchi, Keita Karube, Tetsuji Ogawa, Tetsunori Kobayashi

In this work, to promote the word-level representation learning in end-to-end ASR, we propose a hierarchical conditional model that is based on connectionist temporal classification (CTC).

Automatic Speech Recognition Representation Learning

Improved Mask-CTC for Non-Autoregressive End-to-End ASR

no code implementations26 Oct 2020 Yosuke Higuchi, Hirofumi Inaguma, Shinji Watanabe, Tetsuji Ogawa, Tetsunori Kobayashi

While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.

Automatic Speech Recognition Translation

Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict

no code implementations18 May 2020 Yosuke Higuchi, Shinji Watanabe, Nanxin Chen, Tetsuji Ogawa, Tetsunori Kobayashi

In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.

Audio and Speech Processing Sound

Word Attribute Prediction Enhanced by Lexical Entailment Tasks

no code implementations LREC 2020 Mika Hasegawa, Tetsunori Kobayashi, Yoshihiko Hayashi

Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes.

Lexical Entailment

Towards Answer-unaware Conversational Question Generation

no code implementations WS 2019 Mao Nakanishi, Tetsunori Kobayashi, Yoshihiko Hayashi

Conversational question generation is a novel area of NLP research which has a range of potential applications.

Question Generation

Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension

no code implementations COLING 2018 Mao Nakanishi, Tetsunori Kobayashi, Yoshihiko Hayashi

However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions.

Machine Reading Comprehension Question Answering

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